One of the major problems presently encountered by users of NMR imaging equipment is the motion artifacts, particularly in the images of the upper thorax and the abdomen and a reduction of the motion artifacts is a goal of suppliers of the NMR imaging equipment. A primary cause of the motion artifacts is breathing. Breathing introduces quasi-cyclic changes in the RF data signals received by the NMR system's receiver. This "foreign" frequency causes a number of copies of the images, called "shadows" to appear along the encoding axis, blurring the image and causing artifacts. As these quasi-cyclic changes result from non-linear motions along all three axes, to date no software post-acquisition processing method has been discovered that is effective in correcting the resulting artifacts.
The prior art reveals numerous approaches and methods which have been tried in attempts to minimize the artifacts caused by the breathing of the subject during the NMR imaging scan process. For example, post acquisition data processing methods have been tried to reduce the artifacts. This approach however in addition to the aforementioned problem of three dimensional motion, inherently requires significantly more time per patient. The time is required for the post-acquisition processing of the data.
See for example the technical note entitled "Respiratorily Ordered Phase Encoding (ROPE): A Method for Reducing Motion Artifacts in MR Imaging" by D. R. Bailes et al pp 835-838, Journal of Computer Assisted Tomography, Vol. 9 (4) Jul./Aug. 1985; U.S. Pat. Nos. 4,564,017 and 4,567,893.
In the past, those skilled in the art attempted to minimize the motion artifacts caused by breathing by various breathing gating schemes. A serious drawback in the use of gating schemes, among other things, is that respiratory gating requires additional sophisticated and expensive equipment to generate gating signals and also requires appreciably longer data acquisition time periods with consequent reduced throughput. Since, "throughput" is a key requirement of any NMR system, scientists in the field are continually seeking to shorten the time required for the examination using "gating" or to find faster alternatives to such time intensive prior art processes.
Gating comprises waiting with the pulse train until the selected thoracic position occurs. This means that there is no exact repetition time TR but rather the repetition is controlled by the breathing. Gating thus limits the user, as TR is an important factor affecting image quality. Its control is usually left to the user as a tool in selecting the type of contrast. In gating, TR is a few seconds, instead of the usual TR time of under a second, causing the gated study to last much longer then a non-gated one.
More recently methods have been tried which allow the user to fix TR, but couple the encoding pulse's amplitude to the thoracic position, instead of linearly increasing the amplitude at each pulse repetition, as is the usual procedure. A popular method makes the encoding amplitude a monotonic function of the thoracic position. Thus, after reordering the pulses according to the encoding pulse amplitude, most of the effects of the breathing frequency are eliminated.
Another approach is to use the parity of repetition (whether it is an odd or even one) to select the encoding amplitude. The parity scheme creates shadows (artifacts) that occur a half-image away.
Making the encoding pulse amplitude a simple (linear, say) function of the thoracic position introduces new problems. Some positions are more likely than others and will probably repeat before the less likely positions occur the first time. This "wastes" time, whatever is done with the redundant data obtained because of the repetitions (the redundant data can be discarded, averaged with the previous data from the same amplitude, etc.).
Since the repetition time and the breathing frequency are not synchronized some breathing cycle positions will occur a second or a third time (hereinafter called "double sampling") before others have occurred once. That happens because the breathing cycle position is "random" relative to the occurrence of the encoding pulses and also because during the breathing cycle there are sections with relatively slow motion and others with relatively fast motion. The position axis values sensed during the parts of the cycle where the motion is slow are more likely to be detected in a random sampling arrangement than the position axis values where the motion is fast, partially because the slower motion part of the breathing cycle extends over a longer time period.
Using the integral of the temporal probability function of the thoracic position as the mapping function from position to encoding pulse amplitude creates a flat, nearly constant, probability function for the encoding amplitudes (the method is known as "Histogram Equalization"). However, as the thoracic position is a function of the breathing process and is independent of TR, the position is random relative to the pulse train number. The statistical nature of the sampling will therefore still cause some positions to repeat before others occur even once. Thus, this solution is also not efficient enough.
In one particular prior art method used to speed up the process of activating all of the required encoding pulses, the encoding pulse amplitudes per pulse repetition are selected by using "bins" instead of varying the amplitude of each ensuing encoding pulse as a direct function of the thoracic or breathing cycle position. Each bin is defined by a range of breathing or respiratory cycle positions. A range of encoding pulse amplitudes is assigned to each bin. Each received breathing cycle position then determines a bin and the next encoding pulse amplitude is selected from the determined bin.
There may be different methods of selecting the encoding pulse amplitude once the bin is selected. For example, the central amplitude allocated to the bin may be the first amplitude selected when the breathing cycle position first indicates a particular bin. At the second indication of the particular bin, the first amplitude greater than the central amplitude is selected. The third indication of the particular bin selects the encoding pulse amplitude immediately less than the central amplitude. This process continues until all of the encoding pulse amplitudes assigned to the particular bin are used.
The simple bin methods may also increase the data acquisition time. Examine, e.g., the bin method where each bin includes only one encoding amplitude. If a breathing cycle position is sampled which has already been used, the immediate reaction is to skip it. A few sequences of the data could perhaps be skipped without serious loss. However, as more and more encoding amplitudes are used it becomes increasingly more probable that the next sampled position of the breathing cycle will be a double sampling. The probability of sampling a previously unsampled breathing position decreases with time both because less unsampled positions are left and because the more probable positions are usually sampled earlier. The last few encoding amplitudes may therefore require a large number of "aborted" samplings and a very long marginal time to obtain. Larger bins alleviate the problem but do not eliminate it.
The use of different more complicated binning methods may decrease the acquisition time but only partially solves the problem of the artifacts. First of all, the sampling of the breathing cycle position, is random relative to the pulse time. Therefore, one of the bins still will be used up first. The usual case is that this bin has a higher probability of utilization and therefore will be needed again. Thus, a breathing cycle position connected with this bin will be sampled again before the scan period is over with the consequent waste of time.
A further fault of binning is that a step like correction occurs which sets up a series of stepped blocks. These blocks contain "hidden" cyclic components that result in residual shadows. Another relevant problem is the fact that the same breathing cycle position is found twice (inhalation and exhalation) during each breathing cycle except for the extremes, of course. Where more than one slice is required then the other slices will be split into two phases differing by the slice time difference (STD) x(n-1) where n is the number of the slice. The second slice will be phase split by a single STD. The eighth slice will be split by 7 STD's. Since, the STD is in the order of 100 milliseconds, this phase split will cause a noticeable foreign frequency, the amplitude of which approaches that of the breathing motion even for the third slice. This foreign frequency of course results in blurring.
Yet another problem with the binning solution is that it assumes a constant unchanging breathing cycle. In practice breathing cycles tend to vary in amplitude, time and shape. For example the amplitude may decrease thereby eliminating the sampling of certain positions and raising the possibility of corresponding bins not being used entirely or only being partially used. Attempted solutions to these problems raised by varying breathing cycles include limiting the transformation function to a region smaller than that indicated by the breathing amplitude and/or using bins of equal probability rather than bins of equal intervals.
Limiting the transformation function to a region smaller than the breathing cycle tends to waste part of the breathing cycle. Using bins of equal probability suffers because breathing cycle position probabilities also change with variations in the breathing cycle, and the bin borders are then no longer optimal.
The presently available "binning" methods for synchronizing the breathing position and the encoding pulse amplitude also inherently increase the noise regardless of the method used for selecting pulse amplitudes within the bins. The randomness of the breathing position relative to time carries noise in the breathing position axis and in the encoding amplitude axis of the transformation function. Where the samples or the encoding amplitudes obtained by the samplings are spread with a uniform probability over the appropriate interval, the RMS noise will be plus or minus the square root of 1/12 of the bin interval or the size of the amplitudes range associated with the bin. The noise reduces the signal-to-noise ratio, smears the image and has hidden frequencies which may appear as shadows.